Online Learning Using Multiple Times Weight Updating
نویسندگان
چکیده
منابع مشابه
Double Updating Online Learning
In most kernel based online learning algorithms, when an incoming instance is misclassified, it will be added into the pool of support vectors and assigned with a weight, which often remains unchanged during the rest of the learning process. This is clearly insufficient since when a new support vector is added, we generally expect the weights of the other existing support vectors to be updated ...
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In most online learning algorithms, the weights assigned to the misclassified examples (or support vectors) remain unchanged during the entire learning process. This is clearly insufficient since when a new misclassified example is added to the pool of support vectors, we generally expect it to affect the weights for the existing support vectors. In this paper, we propose a new online learning ...
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Kernel-based online learning often exhibits promising empirical performance for various applications according to previous studies. However, it often suffers a main shortcoming, that is, the unbounded number of support vectors, making it unsuitable for handling large-scale datasets. In this paper, we investigate the problem of budget kernel-based online learning that aims to constrain the numbe...
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ژورنال
عنوان ژورنال: Applied Artificial Intelligence
سال: 2020
ISSN: 0883-9514,1087-6545
DOI: 10.1080/08839514.2020.1730623